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Tests for Deterministic Parametric Structural Change in Regression Models

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  • George Kapetanios

    (Queen Mary, University of London)

Abstract

The problem of structural change justifiably attracts considerable attention in econometrics. A number of different paradigms have been adopted ranging from structural breaks which are sudden and rare to time-varying coefficient models which exhibit structural change more frequently and continuously. This paper is concerned with parametric econometric models whose coefficients change deterministically and smoothly over time. In particular we provide and discuss tests for the null hypothesis of no structural change versus the alternative hypothesis of smooth deterministic structural change. We provide asymptotic tests for this null hypothesis. However, the finite sample performance of these tests is not good as they overreject significantly. To address this problem we propose and justify bootstrap based tests. These tests perform well in an extensive Monte Carlo study.

Suggested Citation

  • George Kapetanios, 2005. "Tests for Deterministic Parametric Structural Change in Regression Models," Working Papers 539, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:539
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    References listed on IDEAS

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    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    2. Horowitz, J., 1996. "Bootstrap Critical Values For Tests Based On The Smoothed Maximum Score Estimator," SFB 373 Discussion Papers 1996,44, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Orbe, Susan & Ferreira, Eva & Rodriguez-Poo, Juan, 2005. "Nonparametric estimation of time varying parameters under shape restrictions," Journal of Econometrics, Elsevier, vol. 126(1), pages 53-77, May.
    4. Kapetanios, George, 2007. "Estimating deterministically time-varying variances in regression models," Economics Letters, Elsevier, vol. 97(2), pages 97-104, November.
    5. George Kapetanios, 2004. "The Impact of Large Structural Shocks on Economic Relationships: Evidence from Oil Price Shocks," Working Papers 524, Queen Mary University of London, School of Economics and Finance.
    6. Joel L. Horowitz, 1996. "Bootstrap Critical Values for Tests Based on the Smoothed Maximum Score Estimator," Econometrics 9603003, University Library of Munich, Germany.
    7. Mohsen Pourahmadi, 1988. "STATIONARITY OF THE SOLUTION OF Xt= AtXt‐1+εt AND ANALYSIS OF NON‐GAUSSIAN DEPENDENT RANDOM VARIABLES," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(3), pages 225-239, May.
    8. Kapetanios, George, 2007. "Estimating deterministically time-varying variances in regression models," Economics Letters, Elsevier, vol. 97(2), pages 97-104, November.
    9. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    10. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
    11. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
    12. Horowitz, J. L., 1995. "Bootstrap Methods In Econometrics: Theory And Numerical Performance," SFB 373 Discussion Papers 1995,63, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    More about this item

    Keywords

    Structural change; Non-stationarity; Deterministic time-variation;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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